Overview

Dataset statistics

Number of variables12
Number of observations918
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory317.2 KiB
Average record size in memory353.8 B

Variable types

Numeric5
Categorical6
Boolean1

Alerts

Age is highly correlated with FastingBSHigh correlation
RestingBP is highly correlated with FastingBSHigh correlation
Cholesterol is highly correlated with FastingBS and 1 other fieldsHigh correlation
FastingBS is highly correlated with Age and 2 other fieldsHigh correlation
MaxHR is highly correlated with HeartDiseaseHigh correlation
HeartDisease is highly correlated with Cholesterol and 1 other fieldsHigh correlation
HeartDisease is highly correlated with ST_Slope and 1 other fieldsHigh correlation
ST_Slope is highly correlated with HeartDiseaseHigh correlation
ChestPainType is highly correlated with HeartDiseaseHigh correlation
ChestPainType is highly correlated with ExerciseAngina and 1 other fieldsHigh correlation
MaxHR is highly correlated with ExerciseAngina and 1 other fieldsHigh correlation
ExerciseAngina is highly correlated with ChestPainType and 3 other fieldsHigh correlation
Oldpeak is highly correlated with ExerciseAngina and 2 other fieldsHigh correlation
ST_Slope is highly correlated with OldpeakHigh correlation
HeartDisease is highly correlated with ChestPainType and 3 other fieldsHigh correlation
Cholesterol has 172 (18.7%) zeros Zeros
Oldpeak has 368 (40.1%) zeros Zeros

Reproduction

Analysis started2021-11-14 14:09:40.650439
Analysis finished2021-11-14 14:10:08.385259
Duration27.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.51089325
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2021-11-14T08:10:08.452873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.432616507
Coefficient of variation (CV)0.1762746973
Kurtosis-0.3861396124
Mean53.51089325
Median Absolute Deviation (MAD)7
Skewness-0.1959330287
Sum49123
Variance88.97425416
MonotonicityNot monotonic
2021-11-14T08:10:08.598933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5451
 
5.6%
5842
 
4.6%
5541
 
4.5%
5638
 
4.1%
5738
 
4.1%
5236
 
3.9%
5135
 
3.8%
5935
 
3.8%
6235
 
3.8%
5333
 
3.6%
Other values (40)534
58.2%
ValueCountFrequency (%)
281
 
0.1%
293
 
0.3%
301
 
0.1%
312
 
0.2%
325
0.5%
332
 
0.2%
347
0.8%
3511
1.2%
366
0.7%
3711
1.2%
ValueCountFrequency (%)
772
 
0.2%
762
 
0.2%
753
 
0.3%
747
0.8%
731
 
0.1%
724
 
0.4%
715
 
0.5%
707
0.8%
6913
1.4%
6810
1.1%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.1 KiB
M
725 
F
193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M725
79.0%
F193
 
21.0%

Length

2021-11-14T08:10:08.709725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:08.769712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
m725
79.0%
f193
 
21.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ChestPainType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
ASY
496 
NAP
203 
ATA
173 
TA
 
46

Length

Max length3
Median length3
Mean length2.949891068
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATA
2nd rowNAP
3rd rowATA
4th rowASY
5th rowNAP

Common Values

ValueCountFrequency (%)
ASY496
54.0%
NAP203
22.1%
ATA173
 
18.8%
TA46
 
5.0%

Length

2021-11-14T08:10:08.837059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:08.903503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
asy496
54.0%
nap203
22.1%
ata173
 
18.8%
ta46
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RestingBP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct67
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.3965142
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2021-11-14T08:10:08.997844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.51415412
Coefficient of variation (CV)0.1398386826
Kurtosis3.271250917
Mean132.3965142
Median Absolute Deviation (MAD)10
Skewness0.1798393101
Sum121540
Variance342.7739028
MonotonicityNot monotonic
2021-11-14T08:10:09.133319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120132
14.4%
130118
 
12.9%
140107
 
11.7%
11058
 
6.3%
15055
 
6.0%
16050
 
5.4%
12529
 
3.2%
13520
 
2.2%
11519
 
2.1%
12818
 
2.0%
Other values (57)312
34.0%
ValueCountFrequency (%)
01
 
0.1%
801
 
0.1%
921
 
0.1%
942
 
0.2%
956
 
0.7%
961
 
0.1%
981
 
0.1%
10015
1.6%
1011
 
0.1%
1023
 
0.3%
ValueCountFrequency (%)
2004
 
0.4%
1921
 
0.1%
1902
 
0.2%
1851
 
0.1%
18012
1.3%
1783
 
0.3%
1741
 
0.1%
1722
 
0.2%
17014
1.5%
1652
 
0.2%

Cholesterol
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct222
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.7995643
Minimum0
Maximum603
Zeros172
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2021-11-14T08:10:09.269141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1173.25
median223
Q3267
95-th percentile331.3
Maximum603
Range603
Interquartile range (IQR)93.75

Descriptive statistics

Standard deviation109.3841446
Coefficient of variation (CV)0.5502232611
Kurtosis0.1182084685
Mean198.7995643
Median Absolute Deviation (MAD)46
Skewness-0.6100864307
Sum182498
Variance11964.89108
MonotonicityNot monotonic
2021-11-14T08:10:09.395269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
 
18.7%
25411
 
1.2%
22310
 
1.1%
22010
 
1.1%
2309
 
1.0%
2119
 
1.0%
2169
 
1.0%
2049
 
1.0%
2198
 
0.9%
2468
 
0.9%
Other values (212)663
72.2%
ValueCountFrequency (%)
0172
18.7%
851
 
0.1%
1002
 
0.2%
1101
 
0.1%
1131
 
0.1%
1171
 
0.1%
1231
 
0.1%
1262
 
0.2%
1291
 
0.1%
1311
 
0.1%
ValueCountFrequency (%)
6031
0.1%
5641
0.1%
5291
0.1%
5181
0.1%
4911
0.1%
4681
0.1%
4661
0.1%
4581
0.1%
4171
0.1%
4121
0.1%

FastingBS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.1 KiB
0
704 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0704
76.7%
1214
 
23.3%

Length

2021-11-14T08:10:09.506955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:09.565084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0704
76.7%
1214
 
23.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RestingECG
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
Normal
552 
LVH
188 
ST
178 

Length

Max length6
Median length6
Mean length4.610021786
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowST
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal552
60.1%
LVH188
 
20.5%
ST178
 
19.4%

Length

2021-11-14T08:10:09.633276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:09.702099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
normal552
60.1%
lvh188
 
20.5%
st178
 
19.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaxHR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.8093682
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2021-11-14T08:10:09.784234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile96
Q1120
median138
Q3156
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)36

Descriptive statistics

Standard deviation25.46033414
Coefficient of variation (CV)0.1861008093
Kurtosis-0.44824782
Mean136.8093682
Median Absolute Deviation (MAD)18
Skewness-0.1443594185
Sum125591
Variance648.2286144
MonotonicityNot monotonic
2021-11-14T08:10:09.912843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15043
 
4.7%
14041
 
4.5%
12036
 
3.9%
13033
 
3.6%
16025
 
2.7%
11023
 
2.5%
12521
 
2.3%
12220
 
2.2%
17020
 
2.2%
11516
 
1.7%
Other values (109)640
69.7%
ValueCountFrequency (%)
601
0.1%
631
0.1%
671
0.1%
691
0.1%
701
0.1%
711
0.1%
722
0.2%
731
0.1%
771
0.1%
781
0.1%
ValueCountFrequency (%)
2021
 
0.1%
1951
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.2%
1882
0.2%
1871
 
0.1%
1862
0.2%
1854
0.4%
1844
0.4%

ExerciseAngina
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
547 
True
371 
ValueCountFrequency (%)
False547
59.6%
True371
40.4%
2021-11-14T08:10:09.999968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Oldpeak
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct53
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8873638344
Minimum-2.6
Maximum6.2
Zeros368
Zeros (%)40.1%
Negative13
Negative (%)1.4%
Memory size7.3 KiB
2021-11-14T08:10:10.083391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.6
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.066570151
Coefficient of variation (CV)1.201953595
Kurtosis1.203063684
Mean0.8873638344
Median Absolute Deviation (MAD)0.6
Skewness1.022872022
Sum814.6
Variance1.137571887
MonotonicityNot monotonic
2021-11-14T08:10:10.211344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0368
40.1%
186
 
9.4%
276
 
8.3%
1.553
 
5.8%
328
 
3.1%
1.226
 
2.8%
0.222
 
2.4%
0.519
 
2.1%
1.418
 
2.0%
1.817
 
1.9%
Other values (43)205
22.3%
ValueCountFrequency (%)
-2.61
0.1%
-21
0.1%
-1.51
0.1%
-1.11
0.1%
-12
0.2%
-0.91
0.1%
-0.81
0.1%
-0.71
0.1%
-0.52
0.2%
-0.12
0.2%
ValueCountFrequency (%)
6.21
 
0.1%
5.61
 
0.1%
51
 
0.1%
4.41
 
0.1%
4.22
 
0.2%
48
0.9%
3.81
 
0.1%
3.71
 
0.1%
3.64
0.4%
3.52
 
0.2%

ST_Slope
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size54.0 KiB
Flat
460 
Up
395 
Down
63 

Length

Max length4
Median length4
Mean length3.139433551
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUp
2nd rowFlat
3rd rowUp
4th rowFlat
5th rowUp

Common Values

ValueCountFrequency (%)
Flat460
50.1%
Up395
43.0%
Down63
 
6.9%

Length

2021-11-14T08:10:10.459933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:10.529618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
flat460
50.1%
up395
43.0%
down63
 
6.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HeartDisease
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.1 KiB
1
508 
0
410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1508
55.3%
0410
44.7%

Length

2021-11-14T08:10:10.593152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T08:10:10.650988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1508
55.3%
0410
44.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-14T08:10:06.545559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:09:41.138656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:04.560715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.237068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.891336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.662242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:09:41.594476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:04.699868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.362399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.014669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.793854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:09:41.731460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:04.836120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.508835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.143377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.919561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:09:41.900387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:04.970318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.635313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.284214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:07.045655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:04.420743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.111056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:05.767533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-14T08:10:06.421999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-14T08:10:10.706275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-14T08:10:10.835249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-14T08:10:10.969594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-14T08:10:11.099932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-14T08:10:11.222458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-14T08:10:08.099993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-14T08:10:08.313036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeSexChestPainTypeRestingBPCholesterolFastingBSRestingECGMaxHRExerciseAnginaOldpeakST_SlopeHeartDisease
040MATA1402890Normal172N0.0Up0
149FNAP1601800Normal156N1.0Flat1
237MATA1302830ST98N0.0Up0
348FASY1382140Normal108Y1.5Flat1
454MNAP1501950Normal122N0.0Up0
539MNAP1203390Normal170N0.0Up0
645FATA1302370Normal170N0.0Up0
754MATA1102080Normal142N0.0Up0
837MASY1402070Normal130Y1.5Flat1
948FATA1202840Normal120N0.0Up0

Last rows

AgeSexChestPainTypeRestingBPCholesterolFastingBSRestingECGMaxHRExerciseAnginaOldpeakST_SlopeHeartDisease
90863MASY1401870LVH144Y4.0Up1
90963FASY1241970Normal136Y0.0Flat1
91041MATA1201570Normal182N0.0Up0
91159MASY1641761LVH90N1.0Flat1
91257FASY1402410Normal123Y0.2Flat1
91345MTA1102640Normal132N1.2Flat1
91468MASY1441931Normal141N3.4Flat1
91557MASY1301310Normal115Y1.2Flat1
91657FATA1302360LVH174N0.0Flat1
91738MNAP1381750Normal173N0.0Up0